Person identification using ocular biometrics with liveness detection

Abstract

A method of assessing the identity of a person by one or more of: internal non-visible anatomical structure of an eye represented by the Oculomotor Plant Characteristics (OPC), brain performance represented by the Complex Eye Movement patterns (CEM), iris patterns, and periocular information. In some embodiments, a method of making a biometric assessment includes measuring eye movement of a subject, making an assessment of whether the subject is alive based on the measured eye movement, and assessing a person's identity based at least in part on the assessment of whether the subject is alive. In some embodiments, a method of making a biometric assessment includes measuring eye movement of a subject, assessing characteristics from the measured eye movement, and assessing a state of the subject based on the assessed characteristics.

Description

PRIORITY CLAIM[0001]This application is a continuation-in-part of International Application No. PCT / US2012 / 30912 Entitled: “PERSON IDENTIFICATION USING OCULAR BIOMETRICS”, filed on Mar. 28, 2012, the disclosure of which is incorporated herein by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT[0002]This invention was made with government support under award no. 60NANB10D213 awarded by the National Institute of Standards, the National Science Foundation CAREER Grant #CNS-1250718, the National Institute of Standards and Technology Grants #60NANB10D213 and #60NANB12D234, and the National Science Foundation GRFP Grant #DGE-1144466. The government has certain rights in the invention.BACKGROUND[0003]1. Field[0004]This disclosure is generally related to person identification, and more specifically to methods and systems for identifying persons using ocular biometric information.[0005]2. Description of the Related Art[0006]Accurate, non-intrusive, an...

AI Technical Summary

Problems solved by technology

Many commonly used methods for identity determination have known problems.

For example, password verification has demonstrated many weaknesses in areas of accuracy (the individual typing the password may not actually be its owner), usability (people forget passwords), and security (people write passwords down or create easy-to-hack passwords).

In cases when the user is prompted to select the identification key from a sequence of numerical and graphical symbols, there is a danger of accidental or intentional shoulder surfing performed directly or by use of a hidden camera.

Moreover, such challenges may become specifically pronounced in cases of multi-user environments including shared-workstation use and more contemporary interaction media such as tabletops.

Authentication methods requiring remembrance of information such as symbols and photos have reduced usability, due to the fact that long, sophisticated passwords can be easily forgotten and short passwords are easy to break.

Even biometric methods such as iris and finger print-based authentication may not be completely fraud-proof, since they are based on a human's body characteristics that can be replicated.

However, commercial iris-identification systems may be easy to spoof, and they are also inconvenient and intrusive since they usually require a user to stand very still and very close to the image capturing device.

Accurate estimation of oculomotor plant characteristics is challenging due to the secluded nature of the corresponding anatomical components, which relies on indirect estimation and includes noise and inaccuracies associated with the eye tracking equipment, and also relies on effective classification and filtering of the eye movement signal.

Many biometric technologies are susceptible to attacks in which faked human features (for example, fake fingerprints, facial images, or iris images) are successfully as passed off as authentic.

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